
import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np





class 				UpperCAmelCase :




	'''simple docstring'''





	def 							UpperCamelCase_    (			self   :					Any					):
					torch.manual_seed(0					)
					__A       =					TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"					)

					torch.manual_seed(0					)
					__A       =					AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"					)

					torch.manual_seed(0					)
					__A       =					UNetaDConditionModel(
					    sample_size=32				,layers_per_block=1				,block_out_channels=[32, 64]				,down_block_types=[
					        "ResnetDownsampleBlock2D",
					        "SimpleCrossAttnDownBlock2D",
					    ]				,mid_block_type="UNetMidBlock2DSimpleCrossAttn"				,up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]				,in_channels=3				,out_channels=6				,cross_attention_dim=32				,encoder_hid_dim=32				,attention_head_dim=8				,addition_embed_type="text"				,addition_embed_type_num_heads=2				,cross_attention_norm="group_norm"				,resnet_time_scale_shift="scale_shift"				,act_fn="gelu"				,)
					unet.set_attn_processor(AttnAddedKVProcessor()					)  # For reproducibility tests

					torch.manual_seed(0					)
					__A       =					DDPMScheduler(
					    num_train_timesteps=10_00				,beta_schedule="squaredcos_cap_v2"				,beta_start=0.00_01				,beta_end=0.02				,thresholding=A				,dynamic_thresholding_ratio=0.95				,sample_max_value=1.0				,prediction_type="epsilon"				,variance_type="learned_range"				,)

					torch.manual_seed(0					)
					__A       =					IFWatermarker()

					return {
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "unet": unet,
					    "scheduler": scheduler,
					    "watermarker": watermarker,
					    "safety_checker": None,
					    "feature_extractor": None,
					}






	def 							UpperCamelCase_    (			self   :					int					):
					torch.manual_seed(0					)
					__A       =					TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"					)

					torch.manual_seed(0					)
					__A       =					AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"					)

					torch.manual_seed(0					)
					__A       =					UNetaDConditionModel(
					    sample_size=32				,layers_per_block=[1, 2]				,block_out_channels=[32, 64]				,down_block_types=[
					        "ResnetDownsampleBlock2D",
					        "SimpleCrossAttnDownBlock2D",
					    ]				,mid_block_type="UNetMidBlock2DSimpleCrossAttn"				,up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]				,in_channels=6				,out_channels=6				,cross_attention_dim=32				,encoder_hid_dim=32				,attention_head_dim=8				,addition_embed_type="text"				,addition_embed_type_num_heads=2				,cross_attention_norm="group_norm"				,resnet_time_scale_shift="scale_shift"				,act_fn="gelu"				,class_embed_type="timestep"				,mid_block_scale_factor=1.4_14				,time_embedding_act_fn="gelu"				,time_embedding_dim=32				,)
					unet.set_attn_processor(AttnAddedKVProcessor()					)  # For reproducibility tests

					torch.manual_seed(0					)
					__A       =					DDPMScheduler(
					    num_train_timesteps=10_00				,beta_schedule="squaredcos_cap_v2"				,beta_start=0.00_01				,beta_end=0.02				,thresholding=A				,dynamic_thresholding_ratio=0.95				,sample_max_value=1.0				,prediction_type="epsilon"				,variance_type="learned_range"				,)

					torch.manual_seed(0					)
					__A       =					DDPMScheduler(
					    num_train_timesteps=10_00				,beta_schedule="squaredcos_cap_v2"				,beta_start=0.00_01				,beta_end=0.02				,)

					torch.manual_seed(0					)
					__A       =					IFWatermarker()

					return {
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "unet": unet,
					    "scheduler": scheduler,
					    "image_noising_scheduler": image_noising_scheduler,
					    "watermarker": watermarker,
					    "safety_checker": None,
					    "feature_extractor": None,
					}






	def 							UpperCamelCase_    (			self   :					List[str]					):
					__A       =					self.get_dummy_components()
					__A       =					self.pipeline_class(**A					)
					pipe.to(A					)
					pipe.set_progress_bar_config(disable=A					)

					__A       =					self.get_dummy_inputs(A					)

					__A       =					inputs["prompt"]
					__A       =					inputs["generator"]
					__A       =					inputs["num_inference_steps"]
					__A       =					inputs["output_type"]

					if "image" in inputs:
									__A       =					inputs["image"]
					else:
									__A       =					None

					if "mask_image" in inputs:
									__A       =					inputs["mask_image"]
					else:
									__A       =					None

					if "original_image" in inputs:
									__A       =					inputs["original_image"]
					else:
									__A       =					None

					__A     ,				__A       =					pipe.encode_prompt(A					)

					# inputs with prompt converted to embeddings
					__A       =					{
					    "prompt_embeds": prompt_embeds,
					    "negative_prompt_embeds": negative_prompt_embeds,
					    "generator": generator,
					    "num_inference_steps": num_inference_steps,
					    "output_type": output_type,
					}

					if image is not None:
									__A       =					image

					if mask_image is not None:
									__A       =					mask_image

					if original_image is not None:
									__A       =					original_image

					# set all optional components to None
					for optional_component in pipe._optional_components:
									setattr(A				,A				,A					)

					__A       =					pipe(**A					)[0]

					with tempfile.TemporaryDirectory() as tmpdir:
									pipe.save_pretrained(A					)
									__A       =					self.pipeline_class.from_pretrained(A					)
									pipe_loaded.to(A					)
									pipe_loaded.set_progress_bar_config(disable=A					)

					pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()					)  # For reproducibility tests

					for optional_component in pipe._optional_components:
									self.assertTrue(
									    getattr(A				,A					) is None				,f'''`{optional_component}` did not stay set to None after loading.'''				,)

					__A       =					self.get_dummy_inputs(A					)

					__A       =					inputs["generator"]
					__A       =					inputs["num_inference_steps"]
					__A       =					inputs["output_type"]

					# inputs with prompt converted to embeddings
					__A       =					{
					    "prompt_embeds": prompt_embeds,
					    "negative_prompt_embeds": negative_prompt_embeds,
					    "generator": generator,
					    "num_inference_steps": num_inference_steps,
					    "output_type": output_type,
					}

					if image is not None:
									__A       =					image

					if mask_image is not None:
									__A       =					mask_image

					if original_image is not None:
									__A       =					original_image

					__A       =					pipe_loaded(**A					)[0]

					__A       =					np.abs(to_np(A					) - to_np(A					)					).max()
					self.assertLess(A				,1E-4					)







	def 							UpperCamelCase_    (			self   :					Any					):
					__A       =					self.get_dummy_components()
					__A       =					self.pipeline_class(**A					)
					pipe.to(A					)
					pipe.set_progress_bar_config(disable=A					)

					__A       =					self.get_dummy_inputs(A					)
					__A       =					pipe(**A					)[0]

					with tempfile.TemporaryDirectory() as tmpdir:
									pipe.save_pretrained(A					)
									__A       =					self.pipeline_class.from_pretrained(A					)
									pipe_loaded.to(A					)
									pipe_loaded.set_progress_bar_config(disable=A					)

					pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()					)  # For reproducibility tests

					__A       =					self.get_dummy_inputs(A					)
					__A       =					pipe_loaded(**A					)[0]

					__A       =					np.abs(to_np(A					) - to_np(A					)					).max()
					self.assertLess(A				,1E-4					)



lowercase			:				Dict   =     [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]






def        snake_case__		(    lowerCamelCase_		):
							A      :					List[str]      		=					0
							while number:
														# Increased Speed Slightly by checking every 5 digits together.
														sum_of_digits_squared += DIGITS_SQUARED[number % 100000]
														number //= 100000

							return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
lowercase			:				list[bool | None]   =     [None] * 10_00_00_00
lowercase			:				int   =     True
lowercase			:				Tuple   =     False






def        snake_case__		(    lowerCamelCase_		):

							if CHAINS[number - 1] is not None:
														return CHAINS[number - 1]  # type: ignore

							A      :					int      		=					chain(next_number(lowerCamelCase_		)		)
							A      :					Dict      		=					number_chain

							while number < 10000000:
														A      :					Any      		=					number_chain
														number *= 10

							return number_chain






def        snake_case__		(    lowerCamelCase_ = 10000000		):
							for i in range(1     ,				lowerCamelCase_		):
														if CHAINS[i] is None:
																					chain(i + 1		)

							return CHAINS[:number].count(lowerCamelCase_		)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(F"{solution() = }")






